U.S. patent number 8,176,046 [Application Number 12/604,164] was granted by the patent office on 2012-05-08 for system and method for identifying trends in web feeds collected from various content servers.
This patent grant is currently assigned to FWIX, Inc.. Invention is credited to Adrian Druzgalski, Andrew Wan.
United States Patent |
8,176,046 |
Druzgalski , et al. |
May 8, 2012 |
System and method for identifying trends in web feeds collected
from various content servers
Abstract
Systems and methods for identifying trends in web feeds
collected from various content servers are disclosed. One
embodiment includes, selecting a candidate phrase indicative of
potential trends in the web feeds, assigning the candidate phrase
to trend analysis agents, analyzing the candidate phrase, by each
of the one or more trend analysis agents, respectively using the
configured type of trending parameter, and/or determining, by each
of the trend analysis agents, whether the candidate phrase meets an
associated threshold to qualify as a potential trended phrase.
Inventors: |
Druzgalski; Adrian (San
Francisco, CA), Wan; Andrew (San Francisco, CA) |
Assignee: |
FWIX, Inc. (San Francisco,
CA)
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Family
ID: |
42109465 |
Appl.
No.: |
12/604,164 |
Filed: |
October 22, 2009 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20100100537 A1 |
Apr 22, 2010 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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61107635 |
Oct 22, 2008 |
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Current U.S.
Class: |
707/731; 707/758;
707/736; 707/706; 706/45; 706/14; 705/7.11; 709/201; 707/722;
709/230; 706/12; 709/217 |
Current CPC
Class: |
G06Q
30/02 (20130101); H04L 12/1859 (20130101); G06Q
10/063 (20130101); H04L 51/16 (20130101) |
Current International
Class: |
G06F
17/00 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Le; Hung
Parent Case Text
CLAIM OF PRIORITY
This application claims priority to U.S. Provisional Patent
Application No. 61/107,635 entitled "METHODS SYSTEMS AND DATA
STRUCTURES FOR PREDICTING TRENDS OF ONLINE FEEDS", which was filed
on Oct. 22, 2008, the contents of which are expressly incorporated
by reference herein.
Claims
What is claimed is:
1. A method of identifying trends in web feeds collected from
various content servers to be presented to a user on a device via a
hosted interface, comprising: selecting a candidate phrase
indicative of potential trends in the web feeds; assigning the
candidate phrase to trend analysis agents; wherein, each the trend
analysis agents is configured to analyze the candidate phrase using
a configured type of trending parameter; analyzing the candidate
phrase, by each of the one or more trend analysis agents,
respectively using the configured type of trending parameter;
determining, by each of the trend analysis agents, whether the
candidate phrase meets an associated threshold to qualify as a
potential trended phrase; wherein, the candidate phrase is assigned
to a trend analysis agent that is of a first state when an
evaluation threshold has not yet been met; and wherein, for a first
agent of the trend analysis agents that determined that the
associated threshold has been met and is of a second state,
selecting another agent; in response to detecting that the another
agent is of the second state when the evaluation threshold has been
reached and that the another agent reached the evaluation threshold
analyzing the same candidate phrase, changing the state of the
first agent back to the first state.
2. The method of claim 1, wherein, for a second agent of the trend
analysis agents that determined that the associated threshold has
not been met and is of a first state, selecting another agent;
wherein, the another agent is configured to use a same trending
parameter for analysis as the second agent; in response to
detecting that the another trend analysis agent having reached the
evaluation threshold, changing the state of the second agent to the
second state.
3. The method of claim 2, further comprising: detecting a first
candidate phrase which the another trend analysis agent reached
evaluation threshold with; assigning the first candidate phrase to
the second agent.
4. The method of claim 3, further comprising, performing a search
on the candidate phrase; determining whether a number of search
results retrieved using the candidate phrase exceeds a
predetermined value; in response to determining that the number of
search results is less than the predetermined value, eliminating
the candidate phrase as being a candidate for trended phrase or a
trended phrase.
5. The method of claim 4, further comprising, eliminating the
candidate phrase from being a trended phrase based on a quantity of
search results generated using the candidate phrase.
6. The method of claim 2, further comprising: in response to
detecting that the another trend analysis agent has not reached the
evaluation threshold, maintaining the state of the second agent in
the first state.
7. The method of claim 2, further comprising, identifying the
candidate phrase as the trended phrase based on a number of trend
analysis agents assigned to analyze the candidate phase that are in
the second state.
8. The method of claim 1, further comprising, identifying the
candidate phrase as the trended phrase that corresponds to an
identified trend if the candidate phrase is determined by at least
two of the trend analysis agents of differently configured types to
have met the associated thresholds.
9. The method of claim 1, further comprising, assigning, by each of
the one or more trend analysis agents, a score to the candidate
phrase; wherein, the candidate phrase is determined to qualify as
the potential trended phrase when the score exceeds the associated
threshold.
10. The method of claim 9, further comprising, aggregating a number
of trend analysis agents that determined that the candidate phrase
qualifies as the trended phrase; aggregating a number of
differently configured types of the trend analysis agents that
determined that the candidate phrase qualifies as the trended
phrase.
11. The method of claim 10, further comprising, assigning a
weighted score to the candidate phrase based on the number of trend
analysis agents; determining, by the weighted score, whether the
identified trend which is associated with the candidate phrase is a
weak or strong trend.
12. The method of claim 10, further comprising, assigning a
weighted score to the candidate phrase based on the number of
differently configured types of the trend analysis agents;
determining, by the weighted score, whether the identified trend
associated with the candidate phrase is a weak or strong trend.
13. The method of claim 1, wherein, in detecting the identified
trend, presenting trended feeds having content related to the
actual trend as having a higher priority in the hosted
interface.
14. The method of claim 1, wherein, each of the candidate phrases
are randomly assigned to the one or more trend analysis agents.
15. The method of claim 1, wherein the candidate phrase includes
one or more words.
16. The method of claim 1, wherein, the configured type of trending
parameter is one of: a time occurrence frequency of the candidate
phrase and a click occurrence frequency.
17. The method of claim 1, wherein, wherein, the configured type of
trending parameter is one of: a term occurrence frequency in a web
feed, and a document occurrence frequency over a set of web
feeds.
18. The method of claim 17, wherein, the term occurrence frequency
and the document occurrence frequency are computed using a title or
summary of the web feeds.
19. The method of claim 1, further comprising, extracting the
candidate phrase from a title or summary of the web feeds.
20. The method of claim 1, wherein, the candidate phrase is
selected based on the parts-of-speech.
21. The method of claim 20, wherein, the candidate phrase is one
of: a gerund, a infinitive, a proper noun, and a noun-verb
clause.
22. A system for identifying trends in web feeds collected from
various content servers to be presented to a user on a device via a
hosted interface, comprising: means for, selecting a candidate
phrase indicative of potential trends in the web feeds; means for,
assigning the candidate phrase to trend analysis agents; wherein,
each the trend analysis agents is configured to analyze the
candidate phrase using a configured type of trending parameter;
means for, analyzing the candidate phrase, by each of the one or
more trend analysis agents, respectively using the configured type
of trending parameter; means for, determining, by each of the trend
analysis agents, whether the candidate phrase meets an associated
threshold to qualify as a potential trended phrase; means for,
selecting another agent of the first state and assigning the
candidate phrase to the another agent for analysis, for a first
agent of the trend analysis agents that determined that the
associated threshold has been met and is of a second state; means
for, changing the state of the first agent back to the first state
in response to the another agent reaching the evaluation threshold;
means for, selecting another agent of the first state and assigning
the candidate phrase to the another agent for analysis, for a
second agent of the trend analysis agents that determined that the
associated threshold has not been met and is of a first state;
wherein, the another agent is configured to use a same trending
parameter for analysis as the second agent; means for, changing the
state of the second agent to the second state, in response to the
another trend analysis agent reaching the evaluation threshold;
means for, identifying the candidate phrase as the trended phrase
based on a number of trend analysis agents assigned to analyze the
candidate phase that are of the second state.
23. A system for identifying trended phrases which correspond to
trends in web feeds collected from multiple content servers,
comprising: a repository to store candidate phrases that
potentially correspond to trends in the web feeds; trend analysis
agents instantiated in a computer system; wherein, the trend
analysis agents analyze the candidate phases using trending
parameters and generates trending analysis data relating to each of
the candidate phrases; wherein, the trending analysis data is
stored in the repository and used in identifying trended phrases
from the candidate phrases; wherein, the candidate phrase is
assigned to a trend analysis agent that is of a first state when an
evaluation threshold has not yet been met; and wherein, for a first
agent of the trend analysis agents that determined that the
associated threshold has been met and is of a second state,
selecting another agent; in response to detecting that the another
agent is of the second state when the evaluation threshold has been
reached and that the another agent reached the evaluation threshold
analyzing the same candidate phrase, changing the state of the
first agent back to the first state.
Description
BACKGROUND
Content providers use web feeds to deliver web content, in
particular, web content that is regularly updated to users. Users
can subscribe to web feeds, which typically include links,
headlines, and/or summaries. A user can view the updated content
through the feed subscription using a feed reader.
However, with the plethora of online content sources and feeds
available for subscription, a user may be inundated with feeds and
updates, many of which the user may not be interested in. In
particular, a user may be subscribed to several sources of news
feeds but only interested in stories that are local or stories that
are popular, for example.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates a block diagram of client devices coupled to one
another and a host server capable of aggregating web feeds relevant
to a geographical locale from multiple sources and identifying
trends in the web feeds.
FIG. 2A depicts a block diagram illustrating a host server for
aggregating web feeds relevant to a geographical locale and
identifying trends.
FIG. 2B depicts a block diagram of the trending engine in the host
server.
FIG. 3 depicts a block diagram of the trending parameter
computation engine of the trending engine.
FIG. 4 illustrates an example of a screenshot showing trends
identified from local feeds and web feeds that are relevant to a
selected trend.
FIG. 5 illustrates an example of a flow diagram showing a process
for using trend analysis agents configured with different trend
analysis parameters to detect trends.
FIG. 6 illustrates how factorization of a matrix is used to extract
candidate phrases for use in evaluation of potential trends.
FIG. 7 depicts a flow chart showing example processes for
identifying candidate phrases as a trended phrase using trend
analysis agents.
FIG. 8 depicts a flow chart showing an example process for
analyzing candidate phrases using trend analysis agents.
FIG. 9 depicts a flow chart showing an example process for using
weighted scores to determine whether an identified trend is weak or
strong.
FIG. 10 depicts a flow chart showing an example process for using a
search filter to verify an identified trend.
FIG. 11 depicts a flow chart showing an example process for using a
scoring algorithm to determine whether a candidate phrase
corresponds to an actual trend.
FIG. 12 depicts a flow chart showing an example process for using a
scoring algorithm to determine whether a candidate phrase
corresponds to an actual trend.
FIG. 13 depicts a flow chart showing an example process for
extracting candidate phrases using matrix factorization.
FIG. 14 depicts a flowchart of an example process for using slopes
to predict trends of feed items.
FIG. 15A depicts an example of a table used for tracking popularity
information of feed items over periods of time.
FIG. 15B depicts an example of a graph of the popularity
information for identifying trends.
FIG. 16 shows a diagrammatic representation of a machine in the
example form of a computer system within which a set of
instructions, for causing the machine to perform any one or more of
the methodologies discussed herein, may be executed.
DETAILED DESCRIPTION
The following description and drawings are illustrative and are not
to be construed as limiting. Numerous specific details are
described to provide a thorough understanding of the disclosure.
However, in certain instances, well-known or conventional details
are not described in order to avoid obscuring the description.
References to one or an embodiment in the present disclosure can
be, but not necessarily are, references to the same embodiment;
and, such references mean at least one of the embodiments.
Reference in this specification to "one embodiment" or "an
embodiment" means that a particular feature, structure, or
characteristic described in connection with the embodiment is
included in at least one embodiment of the disclosure. The
appearances of the phrase "in one embodiment" in various places in
the specification are not necessarily all referring to the same
embodiment, nor are separate or alternative embodiments mutually
exclusive of other embodiments. Moreover, various features are
described which may be exhibited by some embodiments and not by
others. Similarly, various requirements are described which may be
requirements for some embodiments but not other embodiments.
The terms used in this specification generally have their ordinary
meanings in the art, within the context of the disclosure, and in
the specific context where each term is used. Certain terms that
are used to describe the disclosure are discussed below, or
elsewhere in the specification, to provide additional guidance to
the practitioner regarding the description of the disclosure. For
convenience, certain terms may be highlighted, for example using
italics and/or quotation marks. The use of highlighting has no
influence on the scope and meaning of a term; the scope and meaning
of a term is the same, in the same context, whether or not it is
highlighted. It will be appreciated that same thing can be said in
more than one way.
Consequently, alternative language and synonyms may be used for any
one or more of the terms discussed herein, nor is any special
significance to be placed upon whether or not a term is elaborated
or discussed herein. Synonyms for certain terms are provided. A
recital of one or more synonyms does not exclude the use of other
synonyms. The use of examples anywhere in this specification
including examples of any terms discussed herein is illustrative
only, and is not intended to further limit the scope and meaning of
the disclosure or of any exemplified term. Likewise, the disclosure
is not limited to various embodiments given in this
specification.
Without intent to further limit the scope of the disclosure,
examples of instruments, apparatus, methods and their related
results according to the embodiments of the present disclosure are
given below. Note that titles or subtitles may be used in the
examples for convenience of a reader, which in no way should limit
the scope of the disclosure. Unless otherwise defined, all
technical and scientific terms used herein have the same meaning as
commonly understood by one of ordinary skill in the art to which
this disclosure pertains. In the case of conflict, the present
document, including definitions will control.
Embodiments of the present disclosure include systems and methods
for identifying trends in web feeds collected from various content
servers.
FIG. 1 illustrates a block diagram of client devices 102A-N (where
N is an integer) coupled to one another and a host server 100
capable of aggregating web feeds relevant to a geographical locale
from multiple sources (e.g., content providers 108) over a network
106 and identifying trends in the web feeds.
The client devices 102A-N can be any system and/or device, and/or
any combination of devices/systems that is able to establish a
connection with another device, a server and/or other systems. The
client devices 102A-N typically include display or other output
functionalities to present data exchanged between the devices to a
user. For example, the client devices and content providers can be,
but are not limited to, a server desktop, a desktop computer, a
computer cluster, a mobile computing device such as a notebook, a
laptop computer, a handheld computer, a mobile phone, a smart
phone, a PDA, a Blackberry device, a Treo, and/or an iPhone, etc.
In one embodiment, the client devices 102A-N are coupled to a
network 106. In some embodiments, the client devices may be
connected to one another.
The network 106, over which the client devices 102A-N couples may
be a telephonic network, an open network, such as the Internet, or
a private network, such as an intranet and/or the extranet. For
example, the Internet can provide file transfer, remote log in,
email, news, RSS, and other services through any known or
convenient protocol, such as, but is not limited to the TCP/IP
protocol, Open System Interconnections (OSI), FTP, UPnP, iSCSI,
NSF, ISDN, PDH, RS-232, SDH, SONET, etc.
The network 106 can be any collection of distinct networks
operating wholly or partially in conjunction to provide
connectivity to the client devices, host server, and may appear as
one or more networks to the serviced systems and devices. In one
embodiment, communications to and from the client devices 102A-N
can be achieved by, an open network, such as the Internet, or a
private network, such as an intranet and/or the extranet. In one
embodiment, communications can be achieved by a secure
communications protocol, such as secure sockets layer (SSL), or
transport layer security (TLS).
The term "Internet" as used herein refers to a network of networks
that uses certain protocols, such as the TCP/IP protocol, and
possibly other protocols such as the hypertext transfer protocol
(HTTP) for hypertext markup language (HTML) documents that make up
the World Wide Web (the web). Content is often provided by content
servers, which are referred to as being "on" the Internet. A web
server, which is one type of content server, is typically at least
one computer system which operates as a server computer system and
is configured to operate with the protocols of the World Wide Web
and is coupled to the Internet. The physical connections of the
Internet and the protocols and communication procedures of the
Internet and the web are well known to those of skill in the
relevant art. For illustrative purposes, it is assumed the network
106 broadly includes anything from a minimalist coupling of the
components illustrated in the example of FIG. 1, to every component
of the Internet and networks coupled to the Internet.
In addition, communications can be achieved via one or more
wireless networks, such as, but is not limited to, one or more of a
Local Area Network (LAN), Wireless Local Area Network (WLAN), a
Personal area network (PAN), a Campus area network (CAN), a
Metropolitan area network (MAN), a Wide area network (WAN), a
Wireless wide area network (WWAN), Global System for Mobile
Communications (GSM), Personal Communications Service (PCS),
Digital Advanced Mobile Phone Service (D-Amps), Bluetooth, Wi-Fi,
Fixed Wireless Data, 2G, 2.5G, 3G networks, enhanced data rates for
GSM evolution (EDGE), General packet radio service (GPRS), enhanced
GPRS, messaging protocols such as, TCP/IP, SMS, MMS, extensible
messaging and presence protocol (XMPP), real time messaging
protocol (RTMP), instant messaging and presence protocol (IMPP),
instant messaging, USSD, IRC, or any other wireless data networks
or messaging protocols.
The client devices 102A-N can be coupled to the network (e.g.,
Internet) via a dial up connection, a digital subscriber loop (DSL,
ADSL), cable modem, and/or other types of connection. Thus, the
client devices 102A-N can communicate with remote servers (e.g.,
web server, host server, mail server, and instant messaging server)
that provide access to user interfaces of the World Wide Web via a
web browser, for example.
The listing repository 128 and/or the content repository 130 can
store software, descriptive data, images, system information,
drivers, and/or any other data item utilized by parts of the host
server 100 for operation. The repositories 128 and 130 may also
store user information and user content, such as, user profile
information, user preferences, content information, network
information, information/metadata about content sources, raw
content, filtered content, etc. The repositories 128 and 130 may be
managed by a database management system (DBMS), for example but not
limited to, Oracle, DB2, Microsoft Access, Microsoft SQL Server,
PostgreSQL, MySQL, FileMaker, MongoDB, CouchDB, Tokyo Cabinet,
etc.
The repositories 128 and 130 can be implemented via object-oriented
technology and/or via text files, and can be managed by a
distributed database management system, an object-oriented database
management system (OODBMS) (e.g., ConceptBase, FastDB Main Memory
Database Management System, JDOInstruments, ObjectDB, etc.), an
object-relational database management system (ORDBMS) (e.g.,
Informix, OpenLink Virtuoso, VMDS, etc.), a file system, and/or any
other convenient or known database management package. An example
set of data to be stored in the repositories 128 and 130 is further
described with reference to FIG. 2.
The host server 100 is, in some embodiments, able to communicate
with client devices 102A-N and the content providers 108A-N via the
network 106. In addition, the host server 100 is able to retrieve
data from and/or store data in the repositories 128 and 130. The
host server 100 can be implemented on a known or convenient
computer system, such as is illustrated in FIG. 11. The host server
100 is described in more detail with reference to FIG. 2-3.
The content providers 108A-N are coupled to the network 106. The
content providers 108A-N can be implemented on a known or
convenient computer system, such as is illustrated in FIG. 16. The
content providers 108A-N can be a third party site, for example,
including but not limited to, social networking sites, news sites,
blogs, forums, etc. The content providers 108A-N have content
(e.g., articles, images, movies, music, TV shows, feeds, news
feeds, etc.) to be provided to users for access via a user
interface provided by the host server 100. An example screen shot
is illustrated with further reference to the example of FIG. 4.
There could be any number of content providers 108 coupled to the
network 106 that meet these criteria. The content providers 108
make content available to appropriately configured clients coupled
to the network 106. The content may have any applicable known or
convenient form (e.g., multimedia, text, executables, video,
images, audio, etc.), and may or may not be in appropriate form for
delivery to a client through a browser (e.g., on web pages). Users
of client devices 102 can access content (e.g., web feeds, news
feeds), through any applicable known or convenient interface, from
the content provider 108 through the host 100, which aggregates the
web feeds from the multiple providers 108 and performs
filtering.
In the example of FIG. 1, in operation, the host server 100
aggregates web feeds that are relevant to a geographical locale
from multiple sources (e.g., content providers 108). The web feeds
generally include news feeds that are temporally relevant to the
time of access of the web site through which the news feeds are
published. In addition, the web feeds are generally spatially
relevant to a user's geographical location in real-time/near real
time, or a user's specified geographical location.
The aggregation can be performed in a distributed fashion by
multiple machines or engines within the host server 100. In this
manner, each machine or engine is responsible for aggregating feeds
from different sources (e.g., different content providers 108A-N)
to aggregate feeds from more sources over a shorter period of time.
Distributed aggregation using multiple machines can allow web feeds
with time sensitivity (e.g., news feeds) to be aggregated in real
time or near real time.
The host server 100 can, in one embodiment, analyze the web feeds
(e.g., news feeds) and perform selective filtering to ensure that
the published feeds are geographically relevant and/or temporally
relevant. Moreover, filtering can also be performed to ensure the
quality of the content or news content. For example, feeds with
corrupt code (e.g., corrupt HTML code), feed data with broken
links, out-of-date data, feeds with incomplete information can be
removed and/or otherwise prevented from publication to a user. The
host server 100 can also identify one or more images and/or
selectively perform image filtering to publish an image with a
published feed.
In one embodiment, the host server 100 identifies, from the
aggregated feeds, trends. The trends that are identified may be
specific to a local geographical locale or globally relevant. The
trends may be identified by a phrase or word and depicted on the
host interface. In some instances, the phrases or words that are
associated with trends may be clickable or otherwise selectable,
and when selected by a user, causes a search to be performed on the
available web feeds for content relevant to the selected trend. The
host server 100 can then present the search results on the host
interface such that the user can access feeds relevant to the
selected trend. The functions and features associated with the host
server 100 are described with further reference to the examples of
FIG. 2-3.
An ad server 110 may also be coupled to the network 106. The host
server 100 can communicate with the ad server 110 to publish
promotional content through the host user interface on which the
aggregated news feeds are published. The host server 100 can place
advertisements based on the content of the published news feeds
and/or publish advertisements from partnerships with advertisers.
For example, the host server 100 may publish certain ads as
sponsored content for partnered advertisers.
FIG. 2A depicts a block diagram illustrating a host server 200 for
aggregating web feeds relevant to a geographical locale and
identifying trends.
In the example of FIG. 2A, the host server 200 includes a network
interface 202, an aggregator engine 204, a publish server 212,
and/or a trending engine 214. The host server 200 can include one,
or more than one aggregator engine 204 as shown in the example. The
aggregator engine 204 is further illustrated with reference to the
example of FIG. 2B.
The host server 200 can include a listing repository 228 and/or a
content repository 230. The host server 200 may be communicatively
coupled to the listing repository 228 and/or the content repository
230 as illustrated in FIG. 2. In some embodiments, the listing
repository 228 and/or the content repository 230 are partially or
wholly internal to the host server 200.
In the example of FIG. 2, the network controller 202 can be one or
more networking devices that enable the host server 200 to mediate
data in a network with an entity that is external to the host
server, through any known and/or convenient communications protocol
supported by the host and the external entity. The network
controller 202 can include one or more of a network adaptor card, a
wireless network interface card, a router, an access point, a
wireless router, a switch, a multilayer switch, a protocol
converter, a gateway, a bridge, bridge router, a hub, a digital
media receiver, and/or a repeater.
A firewall, can, in some embodiments, be included to govern and/or
manage permission to access/proxy data in a computer network, and
track varying levels of trust between different machines and/or
applications. The firewall can be any number of modules having any
combination of hardware and/or software components able to enforce
a predetermined set of access rights between a particular set of
machines and applications, machines and machines, and/or
applications and applications, for example, to regulate the flow of
traffic and resource sharing between these varying entities. The
firewall may additionally manage and/or have access to an access
control list which details permissions including for example, the
access and operation rights of an object by an individual, a
machine, and/or an application, and the circumstances under which
the permission rights stand.
Other network security functions can be performed or included in
the functions of the firewall, can be, for example, but are not
limited to, intrusion-prevention, intrusion detection,
next-generation firewall, personal firewall, etc. without deviating
from the novel art of this disclosure. In some embodiments, the
functionalities of the network interface 202 and the firewall are
partially or wholly combined and the functions of which can be
implemented in any combination of software and/or hardware, in part
or in whole.
One embodiment of the host server 200 includes one or more
aggregator engines 204. The aggregator engines 204 can be
implemented, example, as software embodied in a computer-readable
medium or computer-readable storage medium on a machine, in
firmware, in hardware, in a combination thereof, or in any
applicable known or convenient device or system. This and other
engines described in this specification are intended to include any
machine, manufacture, or composition of matter capable of carrying
out at least some of the functionality described implicitly,
explicitly, or inherently in this specification, and/or carrying
out equivalent functionality.
The aggregator engines 204 can be any combination of hardware
components and/or software agents able to aggregate web feeds from
various sources using source metadata. Feeds can include by way of
example but not limitation, RSS feeds, Atom, JSON, raw XML, etc.
The aggregator engines 204 can use, for example, identifier
information in the source metadata to identify the location of the
web feed. For example, the identifier information can include a
location identifier such as a URI or URL. In one embodiment, the
aggregator engines 204 are coupled to the listings repository 228.
The listings repository 228 can store listings of sources (e.g.,
content providers in the example of FIG. 1) from which content or
feeds can be collected. The listings repository 228 can store, for
example, in conjunction with source listings, source metadata.
Source metadata can include multiple fields, by way of example but
not limitation, identifier information (e.g., location identifier
(e.g., URL, URI, etc.), unique identifier, a short name, a pretty
name), a geographical identifier with geographical information,
etc. The geographical identifier typically corresponds to a
geographical table having time zones, state, zip codes, airport
IDs, etc. In addition, the source metadata can include a field for
indicating whether the source is used in the aggregation (e.g., a
field that indicates whether the source is active/non-active). In
some embodiments, the source metadata can also include a field with
a short description of the source, a URL construct field, a
latitude field and a longitude field. The construct field is a URL
mapping to include geographical locations identifiers in a URL. For
example a URL with a geographical location identifier may be
http://www.source.com%s->the % s can be associated with a
geographical location (e.g., SF, LA, Seattle, etc.).
The latitude/longitude field can store the location of the actual
story or referenced by the actual story. This is different form the
geographical location field since the geographical location field
typically corresponds to a region (e.g., city, state, neighborhood,
etc.) rather than a specific point such as that referenced by
longitude/latitude data points. The data in the latitude/longitude
field can be used and checked against the geographical location to
verify that the story is relevant to the geographical location
(e.g., the longitude/latitude is within a specific distance
compared to the geographical location).
In one embodiment, the source metadata includes mapping information
which specifies a data structure. For example, the aggregator
engines 204 can parse the collected web feed according to mapping
information contained in the source metadata. In this manner, the
aggregator engines 204 can generate the normalized web feeds that
have a consistent data structure specified by the mapping
information. In one embodiment, the aggregator engine 204 includes
a normalizer module as shown in the example of FIG. 2B, which
normalizes the web feeds into a particular data structure (e.g.,
the data structure specified by the mapping information).
The data structure specified in the mapping information typically
includes multiple fields, including but not limited to, a unique ID
for a story, a title for the story, a link to an image (e.g., a
location identifier for an image), one or more tags, a video link,
a summary field for the story, unassigned fields, a field to
specify the author, a link (e.g., a URL to the story rather than
the source), a field with information used for ranking, a field
with a date (e.g., the date the story or feed was
published/drafted), a field for the source name, geographical
data.
In addition, the data structure can include a field to specify a
source identifier, a time stamp (e.g., to indicate when the
story/feed was aggregated), a geographical identifier, an address
identifier (e.g., an address included with the source), a story
rank, a field to indicate whether the story has been published or
not, a short name of the source, a topic identifier to specify the
category, and/or a field to specify whether the story is active or
not. The data structure can also include fields where popularity or
trending information can be stored. For example, a field can be
used to indicate number of clicks that the story has received,
etc.
Note that the normalization into the data structure can be
performed by automatically crawling the web feed and analyzing each
field. For example, the crawler can use a probabilistic model in
assigning fields in the web feed into the data structure specified
by the mapping information. In one embodiment, certain tags in a
web feed can be determined to correspond to specified fields in the
normalized data structure approximately a certain percentage of the
time. The percentage can be determined based on collecting
statistical information. For example, the tag `summary` in the feed
may be determined to correspond to the summary field in the
normalized data structure .about.60% of the time or the tag
`description` may correspond to the summary field .about.40% of the
time. Such a probabilistic model can be used to automatically map
feeds to the normalized data structure.
In addition, heuristics can be used to perform the mapping. For
example, if the web feed only has a single field with textual data,
then that field is mapped to the title field in the normalized data
structure. If there are two fields with text, then the larger is
typically the summary and the shorter is typically the title. A
field with an URL can be mapped to a link field in the normalized
data structure, for example.
In one embodiment, a human editor performs some or all of the
mapping to generate the normalized web feed, for example by
clicking on links in a feed/story, determining whether the story is
local, evaluating whether the story is interesting, etc. The
automated crawler can be used in conjunction with human editing for
the mapping into the normalized data structure.
One embodiment of the host server 200 includes multiple aggregator
engines 204A-N. In this instance, each of the multiple aggregator
engines 204A-N is assigned a subset of the sources listings. Each
aggregator then collects the web feeds from the assigned sources
using the information contained in the listings repository. The
listings can be assigned to the aggregator engines 204A-N such that
the load is balanced (e.g., each aggregator 204 is assigned an
approximately the same number of listings from which web feeds are
collected). Moreover, the aggregators 204 store the data in a local
cache for future use such that data that has already been fetched
need not be obtained again to conserve bandwidth usage. Therefore,
the aggregators 204 store data in cache such that only new data is
collected from the sources.
The aggregator engine 204 can determine the geographical locale
that is relevant to the web feed. The aggregator engine 204 can
parse through the normalized web feed (e.g., in the title, summary,
the article itself, or other portions of the web feed) to detect
location key words. Location keywords can be any words which may
indicate the presence of an address or other type of location
identifier. For example, location keywords can include,
`St./Street`, `Dr./Drive`, `Blvd./Boulevard`, `th`, `ln./lane`,
etc. Keywords to detect can also include `at`, `on`, `near`, `in`,
`close to`, `in proximity`, etc. Using, the location keywords, the
aggregator engine 204 can identifying an address referenced in the
normalized web feed.
For example, in an article, if the aggregator engine 204 detects
the following location keywords "happened at 3rd St. and Market St.
in San Francisco", the engine 204 can extract the address "3rd",
"Market", and "San Francisco". The aggregator engine 204 can thus
lookup the extracted address in a text file or via a third party
mapping/location service (e.g., Google Maps), for example, to
obtain geographical data such as, GPS coordinates,
longitude/latitude data set, etc. Such geographical data can be
used to determine the geographical locale (e.g., which may be a
neighborhood, a city, a county, a state, a province, a region,
etc.). In some instances, the aggregator engine 204 includes a
geo-locator that determines the location or a relevant locale to
the web feed.
In one embodiment, the aggregator engine 204 performs filtering on
the normalized web feed to determine whether the normalized web
feed includes qualified content for publication. The aggregator
engine 204 can include a filter module and performs one or more of
the filtering procedures. The filtering performed can include one
or more processes performed in parallel or in sequential order.
For example, the aggregator engine 204 can determine
content/stories in the normalized web feed with obscenities or
profanities. Content with identified obscenities or profanities are
typically disqualified from publication to a user. In addition, the
engine 204 can identify ad links in the normalized web feed.
Generally, the content/story with an ad link is also disqualified
from publication. In one embodiment, the disqualified content is
retained in the cache such that it does not get aggregated again in
the future. In addition, disqualified content is typically not
retained in storage. In one embodiment, the ad detection is
performed after the filtering for obscenities or profanities. Ad
detection may also be performed prior to filtering out content with
obscenities or profanities.
The aggregator engine 204 can identify content/story in the
normalized web feed with corrupt data (e.g., corrupt HTML code or
broken links) or unrecognizable characters, for example, to clean
up the content to generate a valid feed for publication. The
corrupt data is typically salvaged or removed. For example, if the
summary is partially corrupt, then the corrupt portions may be
discarded. In some instances, if the title is corrupt and cannot be
salved, then the story may be removed. In addition, the aggregator
engine 204 removes JavaScript before publication. In one
embodiment, the aggregator engine 204 extracts timing data (e.g.,
in a timestamp) from the normalized web feed and uses the timing
data to determine whether the web feed includes content that is
temporally relevant.
Moreover, the aggregator engine 204 can identify an image to be
associated with a published web feed. One embodiment of the
aggregator engine 204 includes an image detector/filter module can
perform some or all of the image identification and association
processes. The image is generally published on a user interface as
being associated with the web feed/qualified content. The image can
be identified from the normalized web feed itself, for example,
using a link to an image contained in the normalized web feed. If
the normalized web feed does not include an image or a link to an
image, the aggregator engine 204 searches for content that were
removed in the filtering process for any images.
An additional image filtering process can be performed by the
aggregator engine 204 as well. For example, the extracted images
can be further filtered for ads, junk icons, banner ads, etc. The
image filter can be applied using dimensions of the images to
determine presence of advertisements or icons in the images. In
addition, the aggregator engine 204 can perform character
recognition to detect text in the images to determine presence of
advertisements in the images to be removed, for example. In one
embodiment, the aggregator engine 204 goes to the link to the
content/story in the web feed and scrapes the HTML for images to
search for images to publish with the feed. The images thus
detected can be filtered using image filters as well. In one
embodiment, the aggregator engine 204 stores the filenames of
extracted images and uses frequently occurring filenames as
filters. For example, names that occur frequently (e.g., ad. jpg)
may be an indicator that the image is an icon or an ad.
One embodiment of the host server 200 includes one or more content
repositories 230A-N. The qualified content/stories/feeds and
associated images thus obtained are stored by the aggregators 204
in the content repository 230A. In some embodiments, the host
server 200 includes multiple content repositories 230A-N to store
the feeds or valid/qualified stories redundantly. For example, each
of the multiple content repositories 230A-N can be individually
coupled to all of the multiple aggregator engines 204 such that
each repository 230 stores all the content aggregated and processed
by each of the aggregators 204. Multiple redundancies ensure that
in the event that a repository malfunctions, the remaining
repositories stores additional copies of the same data. In
addition, with more repositories, more simultaneous connections can
be supported to ensure real time or near real time content delivery
to end users.
One embodiment of the host server 200 includes a publish server 212
coupled to the one or more content repositories 230A-N. The publish
server 212 can be implemented, for example, as software embodied in
a computer-readable medium or computer-readable storage medium on a
machine, in firmware, in hardware, in a combination thereof, or in
any applicable known or convenient device or system. The publish
server 212 can be any combination of hardware components and/or
software agents able to publish the qualified stories accessible to
a user through a user interface.
The publish server 212 typically publishes the stories in a user
interface in a manner such that the stories are shown to be
relevant to a particular geographical locale. An example of a user
interface showing news feeds relevant to a particular locale is
shown in FIG. 9. Each user can be associated with a default
geographical locale which is reconfigurable by the users. Example
features in the user interface is described with further references
to the examples of FIG. 9-14.
In addition, the publish server 212 publishes stories or feeds as
being associated with an image. The image is also retrieved from
one or more of the repositories 230A-N and has typically been
filtered to eliminate ads and icons. When the user selects a
different locale, the publish server 212 communicates with the
repositories to obtain an updated set of stories/feeds that are
relevant to the selected locale. Generally, the published
feeds/stories include temporally relevant content (e.g., news feeds
for publishing in real time or near real time). Thus, the publish
server 212 establishes communications periodically with the
repositories 230 to retrieve updated sets of feeds that have been
aggregated by the aggregator engines 204.
In one embodiment, the publish server 212 labels published feeds as
having been published already so it does not get published again.
In general, the aggregators continuously or periodically aggregate
content. For example, the aggregators can collect feeds
periodically (e.g., after a predetermined amount of time, every 2
minutes, every 5 minutes, every 10 minutes, etc.). The publish
server 212 then retrieves recent content (e.g., temporally relevant
content) from the repositories 230 and publishes them.
In one embodiment, the publish server 212 performs fuzzy matching
on titles of feeds to be published and feeds that have been
published to detect similar content that has previously been
published. For example, fuzzy matching (e.g., Levenshtien distance)
can be performed on feed titles to detect content which may be
previously been published.
In general, the publish server 212 retrieves each distinct
feed/story once. The publish server 212 can determine whether a
story has previously been retrieved for publication by performing a
comparison using the normalized feeds since they are of a uniform
data structure. Moreover, the publish server 212 can determine the
publication time of the content/feed and the aggregation time
(e.g., when the feed/content was processed and aggregated by the
aggregator engines 204) to determine whether the feed/content is
still up to date and temporally relevant.
One embodiment of the host server 200 includes a trending engine
214 coupled to the publish server 212. The trending engine 214 can
be implemented, for example, as software embodied in a
computer-readable medium or computer-readable storage medium on a
machine, in firmware, in hardware, in a combination thereof, or in
any applicable known or convenient device or system. The trending
engine 214 can be any combination of hardware components and/or
software agents able to determine trending data of content or web
feed.
The trending engine 214 can data mine trending data of web feeds or
selected qualified content. The trending data can be used by the
publish server 212 to prioritize placement of feeds or content in
the user interface. For example, content or feeds that are more
popular or contain trendy content/information are typically shown
before less popular content in the user interface. The trending
data can include global trends and/or local trends. For example,
the trending engine 214 can collect global trending data from third
party sites (e.g., Google trends, Twitter trends), social
networking sites (e.g., MySpace, Facebook), etc. Trending data can
also include user trends, determined by the trending engine 214 by,
for example, logging user action and clicks to find the top
read/accessed stories to identify what might be popular and when it
is popular.
In one embodiment, the trending engine 214 identifies trends using
one or more trending parameters. The trending engine 214 uses
multiple trending analysis agents each configured to analyze a
candidate phrase using a different trending parameter to facilitate
in making a determination as to whether the candidate phrase
corresponds to a topic that is popular among feeds in locally or
globally.
The trending engine 214 can select the candidate phrases using any
known or convenient method. For example, the trending engine 214
selects the candidate phrases using parts-of-speech analysis. In
another example, the trending engine 214 selects the candidate
phrases using matrix analysis performed on terms that have a
combination of a high term occurrence frequency in a single
document and a high document occurrence frequency (term that occurs
among many documents). The features related to using trending
analysis agents to identify trends using candidate phrases are
described with further reference to the example of FIG. 2B.
In one embodiment, the trending engine 214 computes the probability
that a feed item would become popular, would remain popular, or
whether an online user will share the feed item with another online
user. For purposes of illustration, the term popularity, as
described herein, refers to any online user action concerning a
particular feed item. For example, a user sharing a feed item with
another user contributes to that feed item's popularity. In other
instances, a user tagging a particular feed item, or providing a
rating on a particular feed item contributes to that particular
feed item's popularity. In other instances, the user's mention of
the feed item in, for example, online polls, contributes to the
feed item's popularity. It is noted that the term popularity is not
confined to positive reviews or favorable feedback of a particular
feed item. Any mention or rating of a feed item, regardless of
whether it is favorable or not, contributes to the popularity
factor.
The trending engine 214 can populate the feed items in a table or a
matrix to perform further prediction operations. An example of such
a table is shown in FIG. 15A. In this example illustrated in FIG.
15A, the feed items 1500 and popularity of feed items at different
time instances 1502 are tabulated. In one embodiment, feed items 1
through n 1520 are tabulated against time instances at five minute
increments 1504. The numbers within the table 1506 correspond to
the popularity of the particular feed item at different time
instances.
After populating the feed item table as illustrated in the
embodiment above, the trending engine 214 computes the graph trends
for each of the feed items. One example of such a graph is
illustrated in FIG. 15B. Here, the popularity of a particular feed
item (n in this example) 1560 is plotted at different time
instances 1565. The slope of the graph is then computed for each
feed item by doing a differential operation on the graph of each
feed item, and example of which is indicated in 1570. By comparing
the differential value (or slope) of the graph at a particular time
instance, and comparing that value against one or more prior time
instances, the trend of the particular feed item is predicted. The
trend prediction is then used in at least one of three ways.
In one embodiment, the predicted trend is used to determine whether
a particular feed item is currently a "fad". The term fad refers to
the gaining popularity of a particular feed item, and can be
determined by using any number of means known to people skilled in
the art. In another embodiment, the predicted trend is used to
determine the probability that a particular feed item will become a
fad within a certain time period. In yet another embodiment, the
predicted trend is used to determine the probability that a current
"fad" item will likely remain a fad item within a predefined time
period.
The components of the host server 200 are a functional unit that
may be divided over multiple computers and/or processing units.
Furthermore, the functions represented by the devices can be
implemented individually or in any combination thereof, in
hardware, software, or a combination of hardware and software.
Different and additional hardware modules and/or software agents
may be included in the host server 200 without deviating from the
spirit of the disclosure.
FIG. 2B depicts a block diagram of the trending engine 214 in the
host server.
The trending engine 214 includes, in one embodiment, a phrase
detector engine 222, a trending parameter computation engine 224, a
selection engine 226, a trend analysis engine 228 having multiple
trend analysis agents, a scoring engine 230, a trend identification
engine 232, and/or a feed prioritization engine 234. Note that
additional or less modules can be included without deviating from
the spirit of the novel disclosure.
One embodiment of the trending engine 214 includes a phrase
detector engine 222. The phrase detector engine 222 can be
implemented, for example, as software embodied in a
computer-readable medium or computer-readable storage medium on a
machine, in firmware, in hardware, in a combination thereof, or in
any applicable known or convenient device or system. The phrase
detector engine 222 can include any combination of hardware
components and/or software agents able to select, identify, detect,
or retrieve candidate phrases which may correspondence to a
potential trend from web feeds.
The phrase detector engine 222 can select or detect phrases using
any known and/or convenient technique. For example, the phrase
detector engine 222 can mark a phrase (e.g., term, word, group of
words, a part of a sentence) as being potentially corresponding to
a trend (e.g., popular content, popular topic, etc.) if it detects
over a certain number of the same phrase in a pool of feeds. The
phrase detector engine 222 can identify candidate phrases by
detecting phrases that occur in over a certain percentage of all
feeds, or phrases that comprise of a certain percentage of all
words in a particular feed, for example. In addition, the phrase
detector engine 222 can select candidate phrase that may correspond
to local or global trends. For example, the phrase detector engine
222 can identify a candidate phrase as potentially corresponding to
a global trend if the phrase or term occurs in feeds relevant to
certain number different geographical locales or certain percentage
of all geographical locales.
In some instances, the candidate phrases are detected from the
title and/or the summary of a web feed. In addition, the candidate
phrases can be detected from the entire feed. In one embodiment,
the detector engine 222 selects a candidate phrase based on the
parts-of-speech of a word. For example, the word/phrase may be
selected as a candidate if it is, a gerund, a infinitive, a proper
noun, and a noun-verb clause. One embodiment of the phrase detector
engine 222 includes a parts-of-speech extractor which can detect
the parts-of-speech of words and phrases in an article or feed and
identify the word and/or phrase as a candidate phrase based on the
parts-of-speech.
In one embodiment, the phrase detector engine 222 selects candidate
phrases by performing vector analysis and factorization on a
matrix. The matrix is typically constructed using words/phrases
that occur frequently in a single feed or across multiple feeds
(e.g., in the title, summary, and/or entire article/document). For
example, the phrase detector engine 222 can generate a
concatenation of a title and a summary of web feeds to compute the
term frequency (e.g., the occurrence of terms in the
concatenation). Using the term frequency, the phrase detector
engine 222 can then use the term frequency to identify a pool of
candidate phrases. In one embodiment, the engine 222 removes
numbers and short words from the concatenation before computing
term frequencies.
To select the words/phrases from the pool of candidate phrases to
construct the matrix, the detector engine 222 can, for each of the
pool of candidates, determine the number of documents across which
occurrence is detected. The detector engine 222 can thus select the
candidate phrases from the pool based on the number of documents.
For example, the detector engine 222 can select the pool of
candidate phrases having a term frequency exceeding a certain
number or having a term frequency in the top x %. The detector
engine 222 can then filter each of the pool of candidate phrases
using the document occurrence frequency. For example, the phrases
having a document frequency exceeding a certain number or having a
term frequency in the top y % of the pool can be selected to
construct as candidate phrases.
Term frequency and document frequency can be used to filter words
and/or phrases. For example, if a particular word/phrase appears in
most or all document (e.g., words like `the`, `a`, `an`, `of`,
etc.), it may be eliminated from being a candidate word/phrase. If
a word/phrase is in one or few documents, then the word/phrase may
be eliminated as well since it is too rare to be a possible
features.
In one embodiment, the phrase detector engine constructs a matrix
with elements that correspond to the term frequency of phrases
cross feeds. The matrix can also be constructed with the term
frequency of words in addition to or in lieu of phrases. For
example the matrix can depict the term frequency of occurrence of
each of the pool of candidates in each story identified by the
story identifiers. An example of such matrix is illustrated in FIG.
6 (e.g., matrix `V`). The phrase detector engine 222 can factorize
the matrix to extract the candidate phrases from the pool of
candidates. The factorization can be performed using non-negative
matrix factorization, singular value decomposition, or any other
optimization based solution such as a genetic algorithm, simulated
annealing, etc. In one embodiment, the engine 222 extracts features
in the feeds (e.g., news) by performing non-negative matrix
factorization. One embodiment of the phrase detector engine 222
includes a vector analyzer which can perform the factorization
and/or construct matrices on which factorization are performed.
One embodiment of the trending engine 214 includes a trend
parameter computation engine 224. The trend parameter computation
engine 224 can be implemented, for example, as software embodied in
a computer-readable medium or computer-readable storage medium on a
machine, in firmware, in hardware, in a combination thereof, or in
any applicable known or convenient device or system. The trend
parameter computation engine 224 can include any combination of
hardware components and/or software agents able to identify,
extract, compute, and/or otherwise determine the value of a
trending parameter for phrases or words.
The trending parameter computation engine 224 ("computation engine
224") can determine the term frequency of occurrence of words or
phrases in a web feed (e.g., in the title, in the summary, in the
entire document, and/or in the concatenation of the title and
summary). For example, the computation engine 224 can compute the
total number of times a certain word or phrase occurs in a document
or in a certain portion of a feed or document. The term frequency,
in some instances, may correspond to how popular a particular topic
is among documents or feeds and can be computed and tracked by
computation engine 224 to identify trends or potential trends. One
embodiment of the computation engine includes a term frequency
computation engine (e.g., the term frequency computation engine 302
in the example of FIG. 3) which can determine the term frequency of
various phrases or words.
In addition, the computation engine 224 computes or otherwise
determines the number of documents or feeds across which occurrence
of a term, phrase, word, or portion of a sentence is detected. The
computation engine 224 can detect the term in the title, summary,
the feed, or a portion of the feed. Occurrence of terms, phrases,
and/or words in documents can indicate popularity of a particular
topic. In one embodiment, since documents or feeds are associated
with geographical locales, by determining the feeds or documents
across which a particular term/phrase exists and the associated
locale, it is possible to detect whether a trend or popular topic
is local or global. In addition, the locale within which the topic
is popular can be determined by tracking document frequency. One
embodiment of the computation engine 224 includes a document
frequency computation engine (e.g., the document frequency
computation engine 304 in the example of FIG. 3) which determines
the number of documents.
In one embodiment, the computation engine 224 determines the time
occurrence frequency of a phrase or word. The time occurrence
frequency can be computed to determine how frequently the phase or
word has occurred in documents (or certain portions of documents
such as the title and/or summary) over a period of time. In some
instances, the time occurrence frequency for one time period can be
compared to that for another time period to determine how the
frequency of occurrence for a word/phrase has changed over time.
This metric maybe an indicator for how quickly a topic became
popular or unpopular and may be used to identify fads in addition
to detecting trends. One embodiment of the trending parameter
computation engine 224 includes a time occurrence frequency tracker
(e.g., the tracker 306 in the example of FIG. 3) that tracks and
computes the time occurrence frequency.
The computation engine 224 tracks clicks and determines the click
occurrence frequency for terms, words, and/or phrases. The click
occurrence can be computed by tracking clicks on stories or feeds.
One embodiment of the trending parameter computation engine 224
includes a click rate tracker (e.g., the click rate tracker 308 in
the example of FIG. 3) that tracks and computes the click rate.
The trending parameter computation engine 224 ("computation engine
224") can include additional engines/modules and is illustrated
with further reference to the example of FIG. 3.
One embodiment of the trending engine 214 includes a selection
engine 226 coupled to the phrase detector engine 222. The selection
engine 226 can be implemented, for example, as software embodied in
a computer-readable medium or computer-readable storage medium on a
machine, in firmware, in hardware, in a combination thereof, or in
any applicable known or convenient device or system. The selection
engine 226 can include any combination of hardware components
and/or software agents able to select the candidate phrase or word
from a pool of phrases (e.g., the phrases or words identified by
the phrase detector engine 222).
In some instances, the candidate phrases are selected by the phrase
detector engine 224. Alternatively, the selection engine 226
selects the candidates using the trending parameters determined by
the trending parameter computation engine 224. For example, the
selection engine 226 can identify the term frequency, document
frequency, time occurrence frequency, and/or click rate from the
computation engine 324 and make a selection based on any one or a
combination of these metrics.
One embodiment of the trending engine 214 includes a trend analysis
engine 228 coupled to the selection engine 226. The trend analysis
engine 228 can be implemented, for example, as software embodied in
a computer-readable medium or computer-readable storage medium on a
machine, in firmware, in hardware, in a combination thereof, or in
any applicable known or convenient device or system. The trend
analysis engine 228 can include any combination of hardware
components and/or software agents able to analyze web feeds to
identify fads, trends, or other types of `popular` topic or
content.
The trend analysis engine 228 can analyze the web feeds using
trending parameters to determine whether candidate phrases
correspond to trends. For example, the trend analysis engine 228
can communicate with the computation engine 224 to determine the
values for the parameters for use in identifying trends by
analyzing candidate phrase. For example, the analysis engine 228
can include multiple trend analysis agents each of which can
analyze candidate phrases using a trending parameter. The candidate
phrases can be assigned to the analysis agents who can analyze the
assigned phrases using a configured type of trending parameter
(e.g., term frequency, document frequency, time occurrence
frequency, and/or click rate, etc.).
For evaluation purposes, the candidate phrase is typically assigned
to an agent of a passive state (or, "first state"). The passive
state (or "first state") refers to the state in which the agent has
not yet evaluated a candidate phrase whereby a threshold has been
met and the active state (or "second state") refers to the state in
which the agent has evaluated a phrase and has met an evaluation
threshold. Generally, there is a different threshold associated
with different types of trending parameters. The agent can generate
a score (e.g., via the scoring module) based on a configured type
of trending parameter and for comparison with the threshold.
Multiple phrases can be assigned to each of the multiple agents in
the first passive state. The agents can then determine whether the
assigned candidate phrase meets an associated threshold to qualify
as a potential trended phrase which corresponds to a trend or
otherwise popular topic.
The collective states of agents (e.g., whether the agents are in
the passive or active state) and the words or phrases that they
became active evaluating are used to identify trended phrases which
correspond to trends. Once the candidate phrases have each been
assigned to agents and have been evaluated, the process proceeds as
follows. For a first agent that has evaluated a phrase/word and
reached the evaluation threshold (e.g., of a second state), the
trend analysis engine 228 selects another agent and determines
whether the selected agent is also in the second state. If so, the
engine 228 determines whether the selected agent reached threshold
analyzing the same phrase/word as the first agent. If so, the first
agent is deactivated back to the first state.
This process is further illustrated diagrammatically in the example
of FIG. 5. The down-weighting allows negative feedback looping to
mitigate the effect of false positives. Since the probability that
an active agent contacts an agent evaluating the same word/phrase
increases with cluster size, the down-weighting keeps groups from
growing too fast and activating more agents than needed.
For a second agent that has evaluated a phrase/word and remains in
the first state (e.g., has not reached evaluation threshold), the
analysis engine 228 selects another agent which is configured to
use the same trending parameter for analysis as the second agent.
The analysis engine 228 can detect whether the selected agent has
reached evaluation threshold and if so, the engine 228 changes the
state of the second agent to an activated state (e.g., second
state). Furthermore, the analysis engine 228 can detect the
candidate phrase/word (e.g., or "hypothesis") which the selected
trend analysis agent reached evaluation threshold with and such
word/phrase is also assigned to the second agent such that the
second agent is now in active same with the same phrase/word.
However, if the selected agent also has not reached evaluation
threshold, the state of the second agent is also maintained in the
first state (inactive state).
This process can be repeated for all agents and is illustrated
diagrammatically in the example of FIG. 5. A candidate phrase can
be identified as a trended phrase based on the number or percentage
of trend analysis agents that are active with the phrase/word
(e.g., having reached threshold in the second state). The agents
may have reached the active state through analyzing the phrase or
through assignment based on the state of a neighboring agent.
One embodiment of the trending engine 214 includes a scoring engine
230 coupled to the trend analysis engine 228 and/or the trend
identification engine 232. The scoring engine 230 can be
implemented, for example, as software embodied in a
computer-readable medium or computer-readable storage medium on a
machine, in firmware, in hardware, in a combination thereof, or in
any applicable known or convenient device or system. The scoring
engine 230 can include any combination of hardware components
and/or software agents able to weight the scores for a candidate
phrase based on collective states of trend analysis agents in the
trend analysis engine 228.
For example, the scoring engine 230 can determine a number or
percentage of trend analysis agents that determines that the
candidate phrase qualifies as the potential trended phrase, or the
number or percentage of agents that have reached evaluation
threshold (e.g., in a second state). Additionally, the scoring
engine 230 can determine the number of differently configured types
of trend analysis agents that determined that the candidate phrase
qualifies as the potential trended phrase. Since the agents can be
configured to analyze phrases using term frequency, document
frequency, term occurrence frequency, click rate or other metrics,
for example, candidate phrases with more agents of differing types
that have reached threshold may typically correspond with stronger
trends.
In one embodiment, the scoring engine 230 assigns a weighted score
to the candidate phrase based on the number or percentage of trend
analysis agents that have reached evaluation threshold or otherwise
is in the second state with the candidate phrase. The scoring
engine 230 can also assign a weighted score to the candidate phrase
based on the number of differently configured types of the trend
analysis agents. For example, if two different types of agents
reached threshold with the candidate phrase, the score may be
multiplied by 2.times.. The trend identification engine 232 can
determine, for example, using the weighted score, whether the
candidate phrase is a weak or strong trend.
One embodiment of the trending engine 214 includes a trend
identification engine 232. The trend ID engine 232 can be coupled
to the trend analysis engine 228, the scoring engine 230, and/or
the feed prioritization engine 234. The trend ID engine 232 can be
implemented, for example, as software embodied in a
computer-readable medium or computer-readable storage medium on a
machine, in firmware, in hardware, in a combination thereof, or in
any applicable known or convenient device or system. The trend ID
engine 232 can include any combination of hardware components
and/or software agents able to identify trends based on the results
of analyzing candidate phrases.
In one embodiment, if the candidate phrase is determined by at
least two of the trend analysis agents of differently configured
types to have met the associated thresholds, the trend ID engine
232 identifying the candidate phrase as a trended phrase that
corresponds to an identified trend. In addition, the trend ID
engine 232 can identify a candidate phrase as a trended phrase
based on a number or percentage of trend analysis agents assigned
to analyze the candidate phase that are in the second state (active
state).
The trend ID engine can also perform a search on a candidate phrase
or a trended phrase and determine whether the number of search
results exceeds a certain value. If the number of search results is
lesser than a certain value, the trend ID engine 232 can eliminate
the phrase as being a candidate for a trended phrase or a trended
phrase. The trend ID engine 232 can also analyze the temporal
characteristics of the search results to determine whether the
feeds or articles are temporally relevant to determine whether the
candidate phrase corresponds to a current trend.
One embodiment of the trending engine 214 includes a feed
prioritization engine 234. The prioritization engine 234 can be
coupled to the trend ID engine 232. The prioritization engine 234
can be implemented, for example, as software embodied in a
computer-readable medium or computer-readable storage medium on a
machine, in firmware, in hardware, in a combination thereof, or in
any applicable known or convenient device or system. The
prioritization engine 234 can include any combination of hardware
components and/or software agents able to present trended feeds in
a host user interface and depict the feeds as being associated with
an identified trend.
In addition, the prioritization engine 234 can present trended
feeds as having a higher priority (e.g., more readily accessible to
a user) in the hosted interface. In one embodiment, the
prioritization engine 234 the trended phrases/words are depicted as
selectable links in the hosted user interface. When a user selects
a trended phrase, the prioritization engine 234 can retrieve feeds
having content related to the actual trend and presents the feeds
in the hosted interface. An example of the hosted user interface
showing trends and feeds relevant to a selected trend is
illustrated with further reference to FIG. 4.
Moreover, the components of the trending engine 214 are a
functional unit that may be divided over multiple computers and/or
processing units. Furthermore, the functions represented by the
devices can be implemented individually or in any combination
thereof, in hardware, software, or a combination of hardware and
software. Different and additional hardware modules and/or software
agents may be included in the host server 200 without deviating
from the spirit of the disclosure.
FIG. 3 depicts a block diagram of the trending parameter
computation engine 324 in the trending engine.
The trending parameter computation engine 324 includes, in one
embodiment, a term frequency computation engine 302, a document
frequency computation engine 304, a time occurrence frequency
tracker 306, and/or a click rate tracker 308. Each module has been
described with reference to the example of FIG. 2B. Note that
additional or less modules can be included without deviating from
the spirit of the novel disclosure.
Moreover, the components of the trending parameter computation
engine 324 are a functional unit that may be divided over multiple
computers and/or processing units. Furthermore, the functions
represented by the devices can be implemented individually or in
any combination thereof, in hardware, software, or a combination of
hardware and software. Different and additional hardware modules
and/or software agents may be included in the trending parameter
computation engine 324 without deviating from the spirit of the
disclosure.
FIG. 4 illustrates an example of a screenshot 400 showing trends
402 identified from feeds and web feeds 406 that are relevant to a
selected trend 404.
The host interface 400 can show the top trends 402 that are
temporally and/or geographically relevant. Each of the trends shown
in 402 can be selected by users to view more feeds or articles
related to that trend. For example, when the trend "market street"
404 is selected, the system automatically performs a search on
"market street" and retrieves feeds 406 that are relevant for
presentation to the user in the interface 400.
Feeds over a certain period of time can be retrieved to be
presented in user interface 400 to ensure that the content is
temporally relevant to an identified trend. In addition, each feed
or article may be published with a time stamp or other indicator
showing when the feed was published. Each feed can be commented on,
shared or emailed to other users. In some instances, the source of
the feed is also shown for each feed entry.
FIG. 5 illustrates an example of a flow diagram 500 showing a
process for using trend analysis agents configured with different
trend analysis parameters to detect trends.
In step 502, word 1 gets assigned to an Agent A of type 1. The type
indicators correspond to a type of trending parameter used by the
agent to perform analysis. In this example, Agent A is also passive
(e.g., state 1) and has not reached evaluation threshold. In most
instances, words are assigned to agents that are in a passive
state.
In step 504, Agent A has performed the analysis (e.g., using a
trending parameter) and determines whether evaluation threshold has
been reached. If so, in step 506, Agent A is then activated with
word 1 (Agent A is now in `state 2 ` in step 508) and in step 526,
the system selects an Agent B. The Agent B may be of any configured
type (e.g., uses any trending parameter to perform its
analysis).
In step 528, the system determines whether Agent B is also active
with the same word 1 as Agent A. If not, Agent A remains active in
step 530. If so, in step 532, Agent A is deactivated (Agent A is
now passive/in `state 1` in step 534).
If at step 504, it is determined that Agent A has not reached
threshold, then in step 512, an Agent C of the same type 1 is
selected. In step 518, it is determined whether Agent C is active.
If not, in step 522, Agent A remains passive. If so, in step 516,
the system determines and retrieves the word (e.g., `Word x` in
514) with which Agent C is active using. In step 520, the Agent A
is then activated with Word x (Agent A is now active with word x in
524).
The same processes for passive and active agents are similarly
repeated and candidate phrases used for identifying trends can be
identified by looking at the states of the agents. The
words/phrases with which the agents became active or inactive while
analyzing can be used in identifying candidate phrases and/or
trended phrases.
FIG. 6 illustrates how factorization of a matrix 602 is used to
extract candidate phrases for use in evaluation of potential
trends.
The example process shown in the diagram of 610 depicts the
extraction of words or phrases from the title and the summary of a
story or feed. From the title and the summary, the term frequency
and document frequency of phrases or words (e.g., `dog`, `cat`,
`cow`, `sheep`, etc.) are determined.
The term frequency and the document frequency are used to select
the words used in the matrix V 602. By performing factorization on
matrix V 602 by any known and/or convenient means (e.g.,
non-negative matrix factorization, singular value decomposition,
etc.), matrices W 604 and H 606 (where V.about.W*H) can be
determined. Using matrices W 604 and H 606, the features of the
feeds can be identified. In addition, candidate phrases and/or
words used for identifying trends in feeds or articles can also be
selected from matrices W 604 and/or H 606.
FIG. 7 depicts a flow chart showing an example process for
identifying candidate phrases as a trended phrase using trend
analysis agents.
In process 702, a candidate phrase indicative of potential trends
in the web feeds is selected. The candidate phrase can be selected
according to any known and/or convenient method. The candidate
phrase can include one or more words or can include any portion of
a sentence.
The candidate phrase can be extracted from the title of an article
or web feed and/or the summary of the article/web feed. The
candidate phrases are in some embodiments selected based on the
frequency of occurrence in the feed or a portion of the feed (e.g.,
in the title and/or the summary). Candidate phrases can also be
selected based on the number of percentage of feeds or articles
across which they occur.
In some instances, the candidate phrase is selected based on the
parts-of-speech, for example, a phrase can be selected if it is one
of: a gerund, a infinitive, a proper noun, or a noun-verb
clause.
In process 704, the candidate phrase is assigned to trend analysis
agents, which are configured to analyze the assigned phrase using a
trending parameter. Each agent is typically configured to perform
the analysis using a configured type of trending parameter, which
includes, by way of example but not limitation, a time occurrence
frequency of the candidate phrase, a click occurrence frequency,
term occurrence frequency in a web feed, and/or a document
occurrence frequency over a set of web feeds. The term occurrence
frequency and the document occurrence frequency can be computed
from a portion of the feeds (e.g., using a title or summary of the
web feeds).
In general, the candidate phrase is randomly assigned to one or
more trend analysis agents. Other candidate phrases can also be
assigned to agents for analysis although typically, an agent is
assigned one phrase to analyze at a time (e.g., until it is
determined whether evaluation threshold was reached for the
phrase). In addition, the candidate phrase is typically assigned to
an agent that is of a "first state" (e.g., when an evaluation
threshold has not yet been met).
In process 706, the candidate phrase is analyzed by each of the one
or more trend analysis agents, respectively using the configured
type of trending parameter. The analysis process is described with
further reference to the example of FIG. 8.
In process 708, it is determined by each of the trend analysis
agents, whether the candidate phrase meets an associated threshold
to qualify as a potential trended phrase. At this stage, whether
the trended phrase is weak or strong can also be determined. This
process is illustrated with further reference to the example of
FIG. 9.
In process 710, the candidate phrase is identified as the trended
phrase based on a number of trend analysis agents assigned to
analyze the candidate phase that are in the second state. After a
trended phrase has been identified, additional filtering processes
can be performed to further verify the validity that a trended
phrase corresponds to a trend. An example of a process for using a
search filter to verify an identified trend is illustrated in FIG.
10. In detecting an identified trend, trended feeds having content
related to the actual trend can be presented as having a higher
priority in the hosted interface. The trended phrases can also be
indicated as such on the hosted interface and may be selectable, as
illustrated in the example screenshot of FIG. 4.
FIG. 8 depicts a flow chart showing an example process for
analyzing candidate phrases using trend analysis agents. This
process is also illustrated in an example diagram shown in FIG.
5.
In process 802, for a first agent that determined that the
associated threshold has been met and is of a "second state",
another agent is selected. In process 804, it is determined whether
the selected agent has reached evaluation threshold (e.g., in the
second state).
If so, in process 806, it is determined whether the selected agent
has reached the evaluation threshold using the same candidate
phrase as the first agent. If so, in process 808, the state of the
first agent is changed back to the first state (e.g., deactivated
state). If not, the state of the first agent is maintained active
(in the second state).
For a second agent that determined that the associated threshold
has not yet been met and is of the first state, another agent is
also selected in process 810. The agent that is selected is
typically configured to use the same trending parameter for
analysis as the second agent. In process 812, it is determined
whether the selected agent has reached evaluation threshold. If
not, in process 820, the state of the second agent is maintained in
the first (inactive) state.
If so, in process 814, the state of the second agent is changed to
the second state (active state). In process 816, a first candidate
phrase which the selected trend analysis agent reached evaluation
threshold with is detected. In process 818, the detected first
candidate phrase is assigned to the second agent such that the
second agent is now active with the first candidate phrase (e.g.,
same as the selected agent).
By repeating this process for multiple agents over multiple
iterations (as illustrated in graphically in the example diagram of
FIG. 5), a candidate phrase can be identified as the trended phrase
based on a number of trend analysis agents assigned to analyze the
candidate phase that are in the second state (e.g., having reached
evaluation threshold). Additionally, candidate phrases can also be
identified as a trended phrase when a percentage or number of
agents is active in the second state. The trended phrases can be
determined as trends.
FIG. 9 depicts a flow chart showing an example process for using
weighted scores to determine whether an identified trend is weak or
strong.
In process 902, each of the trend analysis agents assigns a score
to the candidate phrase. The score can be assigned based on the
analysis using a trending parameter (e.g., term occurrence
frequency, document occurrence frequency, clicks, temporal, etc.).
The scoring algorithm can be different for different trending
parameters (e.g., proportional to term frequency, proportional to
document frequency, proportional to the number of clicks,
proportional to term frequency*document frequency, or proportional
to the inverse of sqrt (t_current-t_previous). T_current typically
corresponds to the current time when analysis is being performed
and t_previous can correspond to when the document/story was
retrieved (e.g., (t_current-tprevious)=how old a story is).
Generally, the candidate phrase can be determined to qualify as a
potential trended phrase when the score exceeds the associated
threshold.
In process 904, a number of trend analysis agents that has
determined that the candidate phrase qualifies as the potential
trended phrase is aggregated. In process 906, a number of
differently configured types of trend analysis agents that have
determined that the candidate phrase qualifies as the potential
trended phrase is aggregated.
In process 908, a weighted score is assigned to the candidate
phrase based on the number of differently configured types (e.g.,
types of trending parameters) of the trend analysis agents that
reached evaluation threshold. In general, the more different types
of agents (e.g., since different trending parameters are used) that
have reached threshold for a phrase, the higher the probability
that the candidate phrase corresponds to a trend or a stronger
trend. Thus, a higher weight is generally assigned to phrases
having agents of differently configured types that reached
threshold.
In some instances, a candidate phrase can be identified as the
trended phrase that corresponds to an identified trend if the
candidate phrase is determined by at least two of the trend
analysis agents of differently configured types to have met the
associated thresholds. Alternatively, three or four different types
may be required for a candidate phrase to qualify as a trended
phrase.
In process 910, a weighted score is assigned to the candidate
phrase based on the number of trend analysis agents. Generally, the
higher the number of agents, the higher the weight. In process 912,
it is determined by the weighted score, whether the identified
trend associated with the candidate phrase is a weak or strong
trend. Typically, a higher weighted score corresponds to a stronger
trend.
FIG. 10 depicts a flow chart showing an example process for using a
search filter to verify an identified trend.
In process 1002, a search on the candidate phrase is performed
among multiple feeds. In process 1004, it is determined whether a
number of search results retrieved using the candidate phrase
exceeds a predetermined value. Since a trend or topic/content that
is otherwise popular would typically have a reasonable number of
related results, if the search result is in significant, the
trended phrase may not correspond to an actual trend. If not, in
process 1006, the candidate phrase is eliminated from being a
trended phrase. The search can be performed on candidate phrases
before they have been selected as trended phrases and/or trended
phrases.
FIG. 11 depicts a flow chart showing an example process for using a
scoring algorithm to determine whether a candidate phrase
corresponds to an actual trend.
In process 1102, a candidate phrase indicative of a potential trend
in the web feeds is identified. In one embodiment, the system
analyzes the titles and/or the summaries of web feeds and tags
parts of speech in the titles and/or summaries. The candidate
phrase can be identified using parts-of-speech analysis. The
candidate phrase can also be selected based on a term frequency of
occurrence of the candidate phrase in the titles and summaries of
the web feeds and/or a document frequency of occurrence of the
candidate phrase across the web feeds. Example processes for
extracting candidate phrases are illustrated with further reference
to FIG. 12.
In process 1104, the web feeds are analyzed using the candidate
phrase according to a trending parameter, for example, according to
any known or convenient method. For example, trending analysis
agents can be used for the analysis. The trending parameter can
include by way of example but not limitation, time occurrence
frequency of the candidate phrase, click occurrence frequency of
the candidate phase, term occurrence frequency in a web feed,
and/or the document occurrence frequency over a set of web
feeds.
In process 1106, a score is assigned to the candidate phrase using
the trending parameter, based on the analysis. In process 1108, it
is determined whether the candidate phrase is a trended phrase that
corresponds to an actual trend, according to the score. In process
1110, trended feeds having content related to the actual trend are
presented as having a higher priority in the hosted interface. In
process 1112, the trended phrase is depicted as a selectable
mechanism in the hosted interface. In process 1114, user selection
of the trended phrase is detected in the user interface. In process
1116, additional feeds having content related to the actual trend
are retrieved and the feeds in are presented the hosted
interface.
FIG. 12 depicts a flow chart showing an example process for using a
scoring algorithm to determine whether a candidate phrase
corresponds to an actual trend.
In process 1202, parts of speeches are tagged in a title or summary
of the web feeds. In process 1204, the candidate phrase is
identified using parts-of-speech analysis. For example, parts of
speeches such as a gerund, a infinitive, a proper noun, and/or a
noun-verb clause can be selected as candidates.
In another example, in process 1206, a concatenation of a title and
a summary of a web feed is generated. In process 1208, numbers
and/or short words are filtered out from the concatenation. In
process 1210, the term frequency of occurrence of terms in the
concatenation is computed. In process 1212, the term frequency of
occurrence is used to identify a pool of candidate phrases. In
process 1214, for each of the pool of candidate phrases, the number
of documents across which occurrence is detected is determined. In
process 1216, the candidate phrases are selected from the pool
based on the number of documents detected in the previous step.
FIG. 13 depicts a flow chart showing an example process for
extracting candidate phrases using matrix factorization.
In process 1302, a matrix is constructed using the term frequency
of occurrence of each of the pool of candidate phrases and story
identifiers (e.g., a document-term/phrase matrix), as shown in the
example process 610 on FIG. 6. An example of such a matrix is
illustrated as matrix V in the example of FIG. 6.
In process 1304, the matrix is factorized. For example, the matrix
V is factorized in to matrices W and H, in the example of FIG. 6.
In process 1306, the candidate phrases are extracted from the pool
of candidates, for example, from the factorized matrix (e.g.,
matrix W or H). In addition, features for the document or feed can
also be extracted from the factorized matrix (e.g., matrix W or
H).
FIG. 14 depicts a flowchart of an example process for using slopes
to predict trends of feed items.
In process 1402, the feed items are collected from a plurality of
webs sources. These web sources include, for example, social
networking sites or product/event review sites, where users or
critics discuss a variety of topics. A feed item, for example, is a
particular topic of discussion. In one example, a feed item could
be a local musical event. Web engines and RSS feeds are used, for
example, to garner feed items from a wide variety of web
sources.
In process 1404, the feed items are populated in a table or a
matrix to perform further prediction operations. An example of such
a table is shown in FIG. 15A. After populating the feed item table,
graph trends for each of the feed items are computed 1406. One
example of such a graph is illustrated in FIG. 15B. By comparing
the differential value (or slope) of the graph at a particular time
instance in process 1406, and comparing that value against one or
more prior time instances in process 1408, the trend of the
particular feed item is predicted. The trend prediction is then
used in at least one of three ways.
In one embodiment, the predicted trend is used to determine whether
a particular feed item is currently a "fad", in process 1410. The
term fad refers to the gaining popularity of a particular feed
item, and can be determined by using any number of means known to
people skilled in the art. In another embodiment, the predicted
trend is used to determine the probability that a particular feed
item will become a fad within a certain time period, in process
1412. In yet another embodiment, the predicted trend is used to
determine the probability that a current "fad" item will likely
remain a fad item within a predefined time period, in process
1414.
FIG. 15A depicts an example of a table used for tracking popularity
information of feed items over periods of time.
In the example table of FIG. 15A, the feed items 1500 and
popularity of feed items at different time instances 1505 are
tabulated. In one embodiment, feed items 1 through n 1520 are
tabulated against time instances at five minute increments 1510.
The numbers within the table 1550 correspond to the popularity of
the particular feed item at different time instances.
FIG. 15B depicts an example of a graph of the popularity
information for identifying trends.
Here, the popularity of a particular feed item (n in this example)
1560 is plotted at different time instances 1565. The slope of the
graph is then computed for each feed item by doing a differential
operation on the graph of each feed item, and example of which is
indicated in 1570. By comparing the differential value (or slope)
of the graph at a particular time instance, and comparing that
value against one or more prior time instances, the trend of the
particular feed item is predicted. The trend prediction is then
used in at least one of three ways.
FIG. 16 shows a diagrammatic representation of a machine in the
example form of a computer system 1600 within which a set of
instructions, for causing the machine to perform any one or more of
the methodologies discussed herein, may be executed.
In the example of FIG. 16, the computer system 1600 includes a
processor, memory, non-volatile memory, and an interface device.
Various common components (e.g., cache memory) are omitted for
illustrative simplicity. The computer system 1500 is intended to
illustrate a hardware device on which any of the components
depicted in the example of FIG. 1 (and any other components
described in this specification) can be implemented. The computer
system 900 can be of any applicable known or convenient type. The
components of the computer system 900 can be coupled together via a
bus or through some other known or convenient device.
The processor may be, for example, a conventional microprocessor
such as an Intel Pentium microprocessor or Motorola power PC
microprocessor. One of skill in the relevant art will recognize
that the terms "machine-readable (storage) medium" or
"computer-readable (storage) medium" include any type of device
that is accessible by the processor.
The memory is coupled to the processor by, for example, a bus. The
memory can include, by way of example but not limitation, random
access memory (RAM), such as dynamic RAM (DRAM) and static RAM
(SRAM). The memory can be local, remote, or distributed.
The bus also couples the processor to the non-volatile memory and
drive unit. The non-volatile memory is often a magnetic floppy or
hard disk, a magnetic-optical disk, an optical disk, a read-only
memory (ROM), such as a CD-ROM, EPROM, or EEPROM, a magnetic or
optical card, or another form of storage for large amounts of data.
Some of this data is often written, by a direct memory access
process, into memory during execution of software in the computer
1100. The non-volatile storage can be local, remote, or
distributed. The non-volatile memory is optional because systems
can be created with all applicable data available in memory. A
typical computer system will usually include at least a processor,
memory, and a device (e.g., a bus) coupling the memory to the
processor.
Software is typically stored in the non-volatile memory and/or the
drive unit. Indeed, for large programs, it may not even be possible
to store the entire program in the memory. Nevertheless, it should
be understood that for software to run, if necessary, it is moved
to a computer readable location appropriate for processing, and for
illustrative purposes, that location is referred to as the memory
in this paper. Even when software is moved to the memory for
execution, the processor will typically make use of hardware
registers to store values associated with the software, and local
cache that, ideally, serves to speed up execution. As used herein,
a software program is assumed to be stored at any known or
convenient location (from non-volatile storage to hardware
registers) when the software program is referred to as "implemented
in a computer-readable medium." A processor is considered to be
"configured to execute a program" when at least one value
associated with the program is stored in a register readable by the
processor.
The bus also couples the processor to the network interface device.
The interface can include one or more of a modem or network
interface. It will be appreciated that a modem or network interface
can be considered to be part of the computer system 1100. The
interface can include an analog modem, isdn modem, cable modem,
token ring interface, satellite transmission interface (e.g.
"direct PC"), or other interfaces for coupling a computer system to
other computer systems. The interface 208 can include one or more
input and/or output devices. The I/O devices can include, by way of
example but not limitation, a keyboard, a mouse or other pointing
device, disk drives, printers, a scanner, and other input and/or
output devices, including a display device. The display device can
include, by way of example but not limitation, a cathode ray tube
(CRT), liquid crystal display (LCD), or some other applicable known
or convenient display device. For simplicity, it is assumed that
controllers of any devices not depicted in the example of FIG. 15
reside in the interface.
In operation, the computer system 1100 can be controlled by
operating system software that includes a file management system,
such as a disk operating system. One example of operating system
software with associated file management system software is the
family of operating systems known as Windows.RTM. from Microsoft
Corporation of Redmond, Wash., and their associated file management
systems. Another example of operating system software with its
associated file management system software is the Linux operating
system and its associated file management system. The file
management system is typically stored in the non-volatile memory
and/or drive unit and causes the processor to execute the various
acts required by the operating system to input and output data and
to store data in the memory, including storing files on the
non-volatile memory and/or drive unit.
Some portions of the detailed description may be presented in terms
of algorithms and symbolic representations of operations on data
bits within a computer memory. These algorithmic descriptions and
representations are the means used by those skilled in the data
processing arts to most effectively convey the substance of their
work to others skilled in the art. An algorithm is here, and
generally, conceived to be a self-consistent sequence of operations
leading to a desired result. The operations are those requiring
physical manipulations of physical quantities. Usually, though not
necessarily, these quantities take the form of electrical or
magnetic signals capable of being stored, transferred, combined,
compared, and otherwise manipulated. It has proven convenient at
times, principally for reasons of common usage, to refer to these
signals as bits, values, elements, symbols, characters, terms,
numbers, or the like.
It should be borne in mind, however, that all of these and similar
terms are to be associated with the appropriate physical quantities
and are merely convenient labels applied to these quantities.
Unless specifically stated otherwise as apparent from the following
discussion, it is appreciated that throughout the description,
discussions utilizing terms such as "processing" or "computing" or
"calculating" or "determining" or "displaying" or the like, refer
to the action and processes of a computer system, or similar
electronic computing device, that manipulates and transforms data
represented as physical (electronic) quantities within the computer
system's registers and memories into other data similarly
represented as physical quantities within the computer system
memories or registers or other such information storage,
transmission or display devices.
The algorithms and displays presented herein are not inherently
related to any particular computer or other apparatus. Various
general purpose systems may be used with programs in accordance
with the teachings herein, or it may prove convenient to construct
more specialized apparatus to perform the methods of some
embodiments. The required structure for a variety of these systems
will appear from the description below. In addition, the techniques
are not described with reference to any particular programming
language, and various embodiments may thus be implemented using a
variety of programming languages.
In alternative embodiments, the machine operates as a standalone
device or may be connected (e.g., networked) to other machines. In
a networked deployment, the machine may operate in the capacity of
a server or a client machine in a client-server network
environment, or as a peer machine in a peer-to-peer (or
distributed) network environment.
The machine may be a server computer, a client computer, a personal
computer (PC), a tablet PC, a laptop computer, a set-top box (STB),
a personal digital assistant (PDA), a cellular telephone, an
iPhone, a Blackberry, a processor, a telephone, a web appliance, a
network router, switch or bridge, or any machine capable of
executing a set of instructions (sequential or otherwise) that
specify actions to be taken by that machine.
While the machine-readable medium or machine-readable storage
medium is shown in an exemplary embodiment to be a single medium,
the term "machine-readable medium" and "machine-readable storage
medium" should be taken to include a single medium or multiple
media (e.g., a centralized or distributed database, and/or
associated caches and servers) that store the one or more sets of
instructions. The term "machine-readable medium" and
"machine-readable storage medium" shall also be taken to include
any medium that is capable of storing, encoding or carrying a set
of instructions for execution by the machine and that cause the
machine to perform any one or more of the methodologies of the
presently disclosed technique and innovation.
In general, the routines executed to implement the embodiments of
the disclosure, may be implemented as part of an operating system
or a specific application, component, program, object, module or
sequence of instructions referred to as "computer programs." The
computer programs typically comprise one or more instructions set
at various times in various memory and storage devices in a
computer, and that, when read and executed by one or more
processing units or processors in a computer, cause the computer to
perform operations to execute elements involving the various
aspects of the disclosure.
Moreover, while embodiments have been described in the context of
fully functioning computers and computer systems, those skilled in
the art will appreciate that the various embodiments are capable of
being distributed as a program product in a variety of forms, and
that the disclosure applies equally regardless of the particular
type of machine or computer-readable media used to actually effect
the distribution.
Further examples of machine-readable storage media,
machine-readable media, or computer-readable (storage) media
include but are not limited to recordable type media such as
volatile and non-volatile memory devices, floppy and other
removable disks, hard disk drives, optical disks (e.g., Compact
Disk Read-Only Memory (CD ROMS), Digital Versatile Disks, (DVDs),
etc.), among others, and transmission type media such as digital
and analog communication links.
Unless the context clearly requires otherwise, throughout the
description and the claims, the words "comprise," "comprising," and
the like are to be construed in an inclusive sense, as opposed to
an exclusive or exhaustive sense; that is to say, in the sense of
"including, but not limited to." As used herein, the terms
"connected," "coupled," or any variant thereof, means any
connection or coupling, either direct or indirect, between two or
more elements; the coupling of connection between the elements can
be physical, logical, or a combination thereof. Additionally, the
words "herein," "above," "below," and words of similar import, when
used in this application, shall refer to this application as a
whole and not to any particular portions of this application. Where
the context permits, words in the above Detailed Description using
the singular or plural number may also include the plural or
singular number respectively. The word "or," in reference to a list
of two or more items, covers all of the following interpretations
of the word: any of the items in the list, all of the items in the
list, and any combination of the items in the list.
The above detailed description of embodiments of the disclosure is
not intended to be exhaustive or to limit the teachings to the
precise form disclosed above. While specific embodiments of, and
examples for, the disclosure are described above for illustrative
purposes, various equivalent modifications are possible within the
scope of the disclosure, as those skilled in the relevant art will
recognize. For example, while processes or blocks are presented in
a given order, alternative embodiments may perform routines having
steps, or employ systems having blocks, in a different order, and
some processes or blocks may be deleted, moved, added, subdivided,
combined, and/or modified to provide alternative or
subcombinations. Each of these processes or blocks may be
implemented in a variety of different ways. Also, while processes
or blocks are at times shown as being performed in series, these
processes or blocks may instead be performed in parallel, or may be
performed at different times. Further any specific numbers noted
herein are only examples: alternative implementations may employ
differing values or ranges.
The teachings of the disclosure provided herein can be applied to
other systems, not necessarily the system described above. The
elements and acts of the various embodiments described above can be
combined to provide further embodiments.
Any patents and applications and other references noted above,
including any that may be listed in accompanying filing papers, are
incorporated herein by reference. Aspects of the disclosure can be
modified, if necessary, to employ the systems, functions, and
concepts of the various references described above to provide yet
further embodiments of the disclosure.
These and other changes can be made to the disclosure in light of
the above Detailed Description. While the above description
describes certain embodiments of the disclosure, and describes the
best mode contemplated, no matter how detailed the above appears in
text, the teachings can be practiced in many ways. Details of the
system may vary considerably in its implementation details, while
still being encompassed by the subject matter disclosed herein. As
noted above, particular terminology used when describing certain
features or aspects of the disclosure should not be taken to imply
that the terminology is being redefined herein to be restricted to
any specific characteristics, features, or aspects of the
disclosure with which that terminology is associated. In general,
the terms used in the claims should not be construed to limit the
disclosure to the specific embodiments disclosed in the
specification, unless the above Detailed Description section
explicitly defines such terms. Accordingly, the actual scope of the
disclosure encompasses not only the disclosed embodiments, but also
all equivalent ways of practicing or implementing the disclosure
under the claims.
While certain aspects of the disclosure are presented below in
certain claim forms, the inventors contemplate the various aspects
of the disclosure in any number of claim forms. For example, while
only one aspect of the disclosure is recited as a
means-plus-function claim under 35 U.S.C. .sctn.112, 6, other
aspects may likewise be embodied as a means-plus-function claim, or
in other forms, such as being embodied in a computer-readable
medium. (Any claims intended to be treated under 35 U.S.C.
.sctn.112, 6 will begin with the words "means for".) Accordingly,
the applicant reserves the right to add additional claims after
filing the application to pursue such additional claim forms for
other aspects of the disclosure.
* * * * *
References